LIPIcs.ICDT.2025.24.pdf
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Learning Tree Pattern Transformations
Authors
Daniel Neider 
,
Leif Sabellek ,
Johannes Schmidt ,
Fabian Vehlken ,
Thomas Zeume
-
Part of:
Volume:
28th International Conference on Database Theory (ICDT 2025)
Series: Leibniz International Proceedings in Informatics (LIPIcs)
Conference: International Conference on Database Theory (ICDT) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2025-03-21
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Document Identifiers
Author Details
- TU Dortmund University, Germany
- Center for Trustworthy Data Science and Security, UA Ruhr, Germany
Acknowledgements
We are grateful to Florin Manea and Markus Schmid for insightful discussions.
Cite As Get BibTex
Daniel Neider, Leif Sabellek, Johannes Schmidt, Fabian Vehlken, and Thomas Zeume. Learning Tree Pattern Transformations. In 28th International Conference on Database Theory (ICDT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 328, pp. 24:1-24:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)
https://doi.org/10.4230/LIPIcs.ICDT.2025.24
Abstract
Explaining why and how a tree t structurally differs from another tree t^⋆ is a question that is encountered throughout computer science, including in understanding tree-structured data such as XML or JSON data. In this article, we explore how to learn explanations for structural differences between pairs of trees from sample data: suppose we are given a set {(t₁, t₁^⋆),… , (t_n, t_n^⋆)} of pairs of labelled, ordered trees; is there a small set of rules that explains the structural differences between all pairs (t_i, t_i^⋆)? This raises two research questions: (i) what is a good notion of "rule" in this context?; and (ii) how can sets of rules explaining a data set be learned algorithmically?
We explore these questions from the perspective of database theory by (1) introducing a pattern-based specification language for tree transformations; (2) exploring the computational complexity of variants of the above algorithmic problem, e.g. showing NP-hardness for very restricted variants; and (3) discussing how to solve the problem for data from CS education research using SAT solvers.
Subject Classification
ACM Subject Classification
- Theory of computation → Complexity theory and logic
- Computing methodologies → Supervised learning
- Theory of computation → Database theory
Keywords
- Tree pattern transformations
- learning from positive examples
- computational complexity
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References
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